User-centric Heterogeneous-action Deep Reinforcement Learning for Virtual Reality in the Metaverse over Wireless Networks

TL;DR

This paper introduces a user-centric deep reinforcement learning algorithm for optimizing frame generation and transmission in VR-based Metaverse systems over wireless networks, enhancing user experience and energy efficiency.

Contribution

It proposes the UCHA algorithm that jointly optimizes channel access, frame generation location, and transmission power considering user heterogeneity in VR Metaverse scenarios.

Findings

UCHA significantly improves user utility across various scenarios.

The algorithm effectively balances energy consumption and quality of experience.

Experimental results validate the superiority of UCHA over baseline methods.

Abstract

The Metaverse is emerging as maturing technologies are empowering the different facets. Virtual Reality (VR) technologies serve as the backbone of the virtual universe within the Metaverse to offer a highly immersive user experience. As mobility is emphasized in the Metaverse context, VR devices reduce their weights at the sacrifice of local computation abilities. In this paper, for a system consisting of a Metaverse server and multiple VR users, we consider two cases of (i) the server generating frames and transmitting them to users, and (ii) users generating frames locally and thus consuming device energy. Moreover, in our multi-user VR scenario for the Metaverse, users have different characteristics and demands for Frames Per Second (FPS). Then the channel access arrangement (including the decisions on frame generation location), and transmission powers for the downlink communications from the server to the users are jointly optimized to improve the utilities of users. This joint optimization is addressed by deep reinforcement learning (DRL) with heterogeneous actions. Our proposed user-centric DRL algorithm is called User-centric Critic with Heterogenous Actors (UCHA). Extensive experiments demonstrate that our UCHA algorithm leads to remarkable results under various requirements and constraints.